


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		List[int]:
    if isinstance(lowerCamelCase_				,					np.ndarray					):
        return list(tensor.shape					)

    _lowercase			:				Optional[int] 						=						tf.shape(lowerCamelCase_					)

    if tensor.shape == tf.TensorShape(lowerCamelCase_					):
        return dynamic

    _lowercase			:				List[Any] 						=						tensor.shape.as_list()

    return [dynamic[i] if s is None else s for i, s in enumerate(lowerCamelCase_					)]



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_ = None				,					lowerCamelCase_ = None					)       ->		tf.Tensor:
    return tf.nn.softmax(logits=logits + 1e-9				,					axis=lowerCamelCase_				,					name=lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=1e-5				,					lowerCamelCase_=-1					)       ->		Union[str, Any]:
    # This is a very simplified functional layernorm, designed to duplicate
    # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
    # models in Transformers.

    if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'					)

    # Get mean and variance on the axis to be normalized
    _lowercase						,							_lowercase			:				Optional[Any] 						=						tf.nn.moments(lowerCamelCase_				,					axes=[axis]				,					keepdims=lowerCamelCase_					)

    if axis != -1:
        # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
        # on every dimension except axis
        _lowercase			:				str 						=						[1] * inputs.shape.rank
        _lowercase			:				List[Any] 						=						shape_list(lowerCamelCase_					)[axis]
        _lowercase			:				List[str] 						=						tf.reshape(lowerCamelCase_				,					lowerCamelCase_					)
        _lowercase			:				Any 						=						tf.reshape(lowerCamelCase_				,					lowerCamelCase_					)

    # Compute layer normalization using the batch_normalization
    # function.
    _lowercase			:				Any 						=						tf.nn.batch_normalization(
        lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					offset=lowerCamelCase_				,					scale=lowerCamelCase_				,					variance_epsilon=lowerCamelCase_				,					)
    return outputs



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0				,					lowerCamelCase_=-1					)       ->		str:
    # Replicates the behavior of torch.flatten in TF

    # If end_dim or start_dim is negative, count them from the end
    if end_dim < 0:
        end_dim += input.shape.rank
    if start_dim < 0:
        start_dim += input.shape.rank

    if start_dim == end_dim:
        return input

    _lowercase			:				Union[str, Any] 						=						tf.shape(lowerCamelCase_					)
    _lowercase			:				str 						=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
    _lowercase			:				Optional[Any] 						=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]				,					axis=0					)
    return tf.reshape(lowerCamelCase_				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		tf.Tensor:
    if not isinstance(lowerCamelCase_				,					tf.Tensor					):
        _lowercase			:				Union[str, Any] 						=						tf.convert_to_tensor(lowerCamelCase_					)  # Catches stray NumPy inputs
    if encoder_attention_mask.shape.rank == 3:
        _lowercase			:				Optional[Any] 						=						encoder_attention_mask[:, None, :, :]
    if encoder_attention_mask.shape.rank == 2:
        _lowercase			:				Union[str, Any] 						=						encoder_attention_mask[:, None, None, :]
    # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
    # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
    # /transformer/transformer_layers.py#L270
    # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
    # encoder_extended_attention_mask.transpose(-1, -2))
    _lowercase			:				Optional[int] 						=						(
        tf.cast(1				,					encoder_attention_mask.dtype					) - encoder_extended_attention_mask
    ) * encoder_extended_attention_mask.dtype.min

    return encoder_extended_attention_mask



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_ = "input_ids"					)       ->		None:
    tf.debugging.assert_less(
        lowerCamelCase_				,					tf.cast(lowerCamelCase_				,					dtype=tensor.dtype					)				,					message=(
            F'''The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCamelCase_					)}) must be smaller than the embedding '''
            F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
        )				,					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				Dict 						=						6_4512
    # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
    # because in that case even chunking the array would not make the saving
    # possible.
    _lowercase			:				Dict 						=						[x for x in data if len(lowerCamelCase_					) > HDF5_OBJECT_HEADER_LIMIT]

    # Expecting this to never be true.
    if bad_attributes:
        raise RuntimeError(
            'The following attributes cannot be saved to HDF5 file because '
            F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
            F'''bytes: {bad_attributes}'''					)

    _lowercase			:				int 						=						np.asarray(lowerCamelCase_					)

    _lowercase			:				List[Any] 						=						1
    _lowercase			:				List[str] 						=						np.array_split(lowerCamelCase_				,					lowerCamelCase_					)

    # This will never loop forever thanks to the test above.
    while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data					):
        num_chunks += 1
        _lowercase			:				Optional[Any] 						=						np.array_split(lowerCamelCase_				,					lowerCamelCase_					)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(lowerCamelCase_					):
            _lowercase			:				Union[str, Any] 						=						chunk_data
    else:
        _lowercase			:				Dict 						=						data



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    if name in group.attrs:
        _lowercase			:				List[str] 						=						[n.decode('utf8'					) if hasattr(lowerCamelCase_				,					'decode'					) else n for n in group.attrs[name]]
    else:
        _lowercase			:				List[str] 						=						[]
        _lowercase			:				List[str] 						=						0
        while "%s%d" % (name, chunk_id) in group.attrs:
            data.extend(
                [n.decode('utf8'					) if hasattr(lowerCamelCase_				,					'decode'					) else n for n in group.attrs['%s%d' % (name, chunk_id)]]					)
            chunk_id += 1
    return data



def 							UpperCamelCase_( lowerCamelCase_					)       ->		str:

    def _expand_single_ad_tensor(lowerCamelCase_					):
        if isinstance(lowerCamelCase_				,					tf.Tensor					) and t.shape.rank == 1:
            return tf.expand_dims(lowerCamelCase_				,					axis=-1					)
        return t

    return tf.nest.map_structure(_expand_single_ad_tensor				,					lowerCamelCase_					)







'''simple docstring'''






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class 				a__:







    def __init__( self					:    List[Any]		,     __snake_case					:    str      ):
      if isinstance(__snake_case		,     __snake_case      ):
        # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
        # modified it, it will not be accepted here again, since `auto` values would have been overridden
        a		:   Union[str, Any]	  =	deepcopy(__snake_case      )
      elif os.path.exists(__snake_case      ):
        with io.open(__snake_case		,     'r'		,     encoding='utf-8'      ) as f:
          a		:   List[str]	  =	json.load(__snake_case      )
      else:
        try:
          a		:   str	  =	baseaa.urlsafe_baadecode(__snake_case      ).decode('utf-8'      )
          a		:   Union[str, Any]	  =	json.loads(__snake_case      )
        except (UnicodeDecodeError, AttributeError, ValueError):
          raise ValueError(
              F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""      )

      a		:   Optional[int]	  =	config

      self.set_stage_and_offload()







    def      lowercase_			( self					:    Any      ):
      # zero stage - this is done as early as possible, before model is created, to allow
      # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
      # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
      a		:   List[str]	  =	self.get_value('zero_optimization.stage'		,     -1      )

      # offload
      a		:   Union[str, Any]	  =	False
      if self.is_zeroa() or self.is_zeroa():
        a		:   Any	  =	set(['cpu', 'nvme']      )
        a		:   Tuple	  =	set(
            [
                self.get_value('zero_optimization.offload_optimizer.device'      ),
                self.get_value('zero_optimization.offload_param.device'      ),
            ]      )
        if len(offload_devices & offload_devices_valid      ) > 0:
          a		:   Optional[int]	  =	True







    def      lowercase_			( self					:    str		,     __snake_case					:    Optional[Any]      ):
      a		:   Any	  =	self.config

      # find the config node of interest if it exists
      a		:   Dict	  =	ds_key_long.split('.'      )
      a		:   str	  =	nodes.pop()
      for node in nodes:
        a		:   Tuple	  =	config.get(__snake_case      )
        if config is None:
          return None, ds_key

      return config, ds_key







    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[int]		,     __snake_case					:    List[str]=None      ):
      a  ,      a		:   int	  =	self.find_config_node(__snake_case      )
      if config is None:
        return default
      return config.get(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Dict		,     __snake_case					:    List[str]		,     __snake_case					:    List[Any]=False      ):
      a		:   Union[str, Any]	  =	self.config

      # find the config node of interest if it exists
      a		:   Tuple	  =	ds_key_long.split('.'      )
      for node in nodes:
        a		:   Optional[int]	  =	config
        a		:   List[str]	  =	config.get(__snake_case      )
        if config is None:
          if must_exist:
            raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""      )
          else:
            return

        # if found remove it
      if parent_config is not None:
        parent_config.pop(__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Dict      ):
      a		:   Optional[Any]	  =	self.get_value(__snake_case      )
      return False if value is None else bool(__snake_case      )







    def      lowercase_			( self					:    str		,     __snake_case					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.get_value(__snake_case      )
      return False if value is None else not bool(__snake_case      )







    def      lowercase_			( self					:    Tuple      ):
      return self._stage == 2







    def      lowercase_			( self					:    str      ):
      return self._stage == 3







    def      lowercase_			( self					:    int      ):
      return self._offload

class 				a__:







    def __init__( self					:    str		,     __snake_case					:    Any      ):
      a		:   Any	  =	engine







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    int		,     **__snake_case					:    List[Any]      ):
      # runs backpropagation and handles mixed precision
      self.engine.backward(__snake_case		,     **__snake_case      )

      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      self.engine.step()
      # and this plugin overrides the above calls with no-ops when Accelerate runs under
      # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
      # training loop that works transparently under many training regimes.

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    Any      ):
      super().__init__(__snake_case		,     device_placement=__snake_case		,     scaler=__snake_case      )
      a		:   List[Any]	  =	hasattr(self.optimizer		,     'overflow'      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    str=None      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







    def      lowercase_			( self					:    int      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







    @property
    def      lowercase_			( self					:    Any      ):
      if self.__has_overflow__:
        return self.optimizer.overflow
      return False

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    str		,     __snake_case					:    List[Any]		,     __snake_case					:    str      ):
      super().__init__(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Union[str, Any]      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

class 				a__:







    def __init__( self					:    Optional[Any]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    int=0.001		,     __snake_case					:    Any=0		,     **__snake_case					:    List[Any]      ):
      a		:   Optional[Any]	  =	params
      a		:   Union[str, Any]	  =	lr
      a		:   Any	  =	weight_decay
      a		:   Any	  =	kwargs

class 				a__:







    def __init__( self					:    Any		,     __snake_case					:    int		,     __snake_case					:    int=None		,     __snake_case					:    Dict=0		,     **__snake_case					:    Optional[int]      ):
      a		:   Optional[int]	  =	optimizer
      a		:   List[str]	  =	total_num_steps
      a		:   Tuple	  =	warmup_num_steps
      a		:   Optional[Any]	  =	kwargs